Intelligent Smart City Energy Ecosystems: A Blockchain-Enabled Peer-to-Peer Renewable Energy Management Framework Using Multi-Agent Deep Reinforcement Learning and Multi-Objective Optimization for Integrated Offshore Wind–Urban Solar Power Systems

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Chinese explanation / 中文解读

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Original abstract

Against the backdrop of smart city construction driven by global carbon neutrality goals, this study first sorts out the core requirements for the intelligent management framework of distributed renewable energy, which are spawned by three core driving trends. It then clarifies the inherent flaws of traditional centralized power systems, including insufficient scheduling flexibility and excessive redundancy losses, as well as the new requirements put forward by emerging peer-to-peer (P2P) energy trading for transaction credibility and the autonomy of participating entities. To address the above industry pain points and adapt to the development requirements of new scenarios, this study proposes an intelligent energy ecosystem framework that integrates three core technologies. Its three core functional modules are: a blockchain-enabled P2P renewable energy trading module, a Multi-Agent Deep Reinforcement Learning (MADRL) control module, and a multi-objective optimization module based on Pareto optimal decision analysis. The energy assets managed in a coordinated way by this framework cover a 150MW-capacity offshore wind farm, an 85MW-capacity urban photovoltaic network, supporting battery energy storage systems, and urban prosumer communities. This study carried out a one-year large-scale simulation test to verify the framework's performance. Compared with the traditional centralized scheduling scheme, the proposed framework achieves multi-dimensional quantitative improvements: total energy cost reduced by 31.8%, P2P transaction volume increased by 42.6%, renewable energy utilization rate raised by 27.4%, carbon emissions cut by 36.1%, supply-demand balance accuracy reaching 97.8%, grid peak dependence reduced by 34.7%, and transaction settlement time shortened by 68%. This study constructs a smart city ecosystem model to conduct economic analysis, and calculates that annual energy saving benefits exceed 12.4 million US dollars, with an investment payback period of 4.2 years; however, this study has limitations, as its conclusions rely on assumptions about the future scalability of blockchain and the performance of communication infrastructure. This study verifies that integrating artificial intelligence (AI), distributed ledger technology, and multi-objective optimization can improve the efficiency, sustainability, and resilience of urban energy systems. The framework proposed in this study can advance the development of smart grids, support the net-zero energy transition, empower prosumers, and help build a sustainable smart city ecosystem.

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